Autonomous Vehicles Camera Blinding Attack Detection Using Sequence Modelling and Predictive Analytics 2020-01-0719
Autonomous vehicles are waiting to address the global automotive mobility challenges through an intelligent smart transportation system, which includes advanced sensor-actuator configurations to control, navigate, and drive the vehicles. Multi-sensor data fusion from the key sensors such as camera, radar, and lidar is used to achieve the environmental perception for autonomous vehicles by capturing the various attributes of the environment. Cameras are the dominant sensors to achieve the perception by providing vision capability to vehicles. The direct interface of the cameras with the dynamic driving environment carries numerous attack surfaces on the camera. Blinding attacks on the cameras are one of the critical attacks with an intention to blind the cameras either fully or partially by projecting light into the cameras to hide the objects which results in failure in object detection. Here, the blinding attack detection approach is proposed which detects the blinding attacks on the camera in a dynamic driving environment by camera data predictive analytics. The proposed system predicts the future next frame of the video at each time instance and compares the received frame from the camera with the predicted frame at that instance to detect the blinding attacks. The incoming frames from the camera are sequentially modeled using a convolutional encoder-decoder neural network to predict the consecutive future frames, and the predicted frames are compared with the received camera frames to identify the similarity measure between the predicted and incoming camera frames of the same instance. Further, the approach detects the blinding on the camera, if the similarity measure calculated falls below a fixed threshold. The similarity measure which is inversely proportional to the amount of blinding is used to identify the blinding attacks. The predictive analytics of the sequentially modeled video frames with similarity measurement is used for the successful detection of blinding attacks.